Machine Learning and Deep Learning are key skills for a Data Scientist! ๐Ÿ”‘ But also for Time Series! ๐Ÿ“ˆ

TOP 5๏ธโƒฃ COURSES to learn about it ๐Ÿ‘‡

๐Ÿ‘‡๐Ÿ‘‡๐Ÿ‘‡๐Ÿ‘‡๐Ÿ‘‡
#python #datascience #ai
1๏ธโƒฃ Start by learning the basics of Machine Learning with this fantastic course.

โ€ข Learn both supervised and unsupervised algorithms
โ€ข Get introduced to Neural Networks
โ€ข Find out about XGBoost

coursera.org/specializationโ€ฆ
2๏ธโƒฃ Learn about Deep Learning:

โ€ข Neural Networks
โ€ข Convolutional Neural Networks (CNN)
โ€ข Sequence models (very useful for Time Series!)

coursera.org/specializationโ€ฆ
3๏ธโƒฃ Put Deep Learning into practice with TensorFlow!

โ€ข Intro to Tensorflow
โ€ข Create your CNN models
โ€ข Learn about Recursive Neural Networks and their variants: GRU and LSTM (great for Time Series!)

coursera.org/professional-cโ€ฆ
4๏ธโƒฃ Deepen your knowledge with this advanced course in TensorFlow.

โ€ข Learn about more complex techniques and models
โ€ข Get introduced to Computer Vision and Generative techniques

coursera.org/specializationโ€ฆ
5๏ธโƒฃ Finally learn how to deploy your models!

โ€ข MLOps
โ€ข Best practices to maintain and monitor your models

coursera.org/specializationโ€ฆ
Please ๐Ÿ”Retweet the first Tweet if you found it useful to increase the reach!

๐Ÿ”” Follow me @daansan_ml if you are interested in:

๐Ÿ #Python
๐Ÿ“Š #DataScience
๐Ÿ“ˆ #TimeSeries
๐Ÿค– #MachineLearning

Thanks! ๐Ÿ˜‰

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More from @daansan_ml

Dec 6
5๏ธโƒฃ YouTube playlists / videos to learn Time Series! โ–ถ๏ธ

Check them out!

๐Ÿงต ๐Ÿ‘‡๐Ÿ‘‡

#Python #MachineLearning #DataScience
1๏ธโƒฃ "Time Series Analysis" by ritvikmath

My personal top 1 recommendation for learning Time Series.

A great combination of theory and code ๐Ÿ‘Œ

youtube.com/playlist?list=โ€ฆ
2๏ธโƒฃ "Time Series" by Aric LaBarr

๐Ÿ‘ Good variety of models. Short videos.

๐Ÿ‘Ž Purely theoretical, no code.

youtube.com/playlist?list=โ€ฆ
Read 7 tweets
Dec 4
What is the difference between seasonality and cycle in Time Series? ๐Ÿค”

๐Ÿงต ๐Ÿ‘‡

#Python #DataScience #MachineLearning Image
๐Ÿ”ดSeasonality refers to regular patterns that occur at a specific frequency, often at a yearly, monthly or weekly interval.
For example:

โ€ข Retail sales tend to increase during the holiday season

โ€ข Electricity consumption tends to be higher in the summer months when people use air conditioning more often
Read 9 tweets
Nov 29
Answer these 8๏ธโƒฃ questions before starting any Time Series project!

๐Ÿ‘‡๐Ÿ‘‡๐Ÿ‘‡๐Ÿ‘‡

#DataScience #MachineLearning #Python Image
1๏ธโƒฃ What are your inputs and outputs to forecast?

๐Ÿ“ฅ Inputs are the historical data you provide to the model

๐Ÿ“ค Outputs are the predictions or forecasts for a future timestep
2๏ธโƒฃ What are your endogenous or exogenous input variables?

โ€ข Endogenous: are influenced by other variables within the system

โ€ข Exogenous: are not and can be considered outside the system

E.g., endogenous could be the number of daily purchases and exogenous the bank holidays.
Read 10 tweets
Nov 28
Build your ARCH model to predict volatility! ๐Ÿ”ฎ

๐Ÿงต ๐Ÿ‘‡

#TimeSeries #MachineLearning #Python #DataScience Image
First, you need to import the required libraries. Image
Now it is time to download the stock data (S&P500) and format it appropriately.

We need to set the frequency to Business days and the index as Datetime. Image
Read 9 tweets
Nov 27
The wait is over! ๐ŸŽ‰

Before moving on to code ARCH models...๐Ÿ‘จโ€๐Ÿ’ป

I will share the notebook in #Python for ARIMA models! ๐Ÿ““

๐Ÿšจ Check the end of the thread, there's a present! ๐ŸŽ

#TimeSeries #DataScience #MachineLearning Image
First, the steps covered:

1๏ธโƒฃ Import data (in this case Google stock price) ๐Ÿ“š

2๏ธโƒฃ Format data ๐Ÿ”จ

3๏ธโƒฃ Visualise prices and returns ๐Ÿ”

4๏ธโƒฃ Estimate parameters p, d and q ๐Ÿ”ฌ
5๏ธโƒฃ Build the initial model ๐Ÿ› ๏ธ

6๏ธโƒฃ Find the optimal model ๐ŸŒŸ

7๏ธโƒฃ Forecast! ๐Ÿ”ฎ
Read 5 tweets
Nov 21
Having missing values is a big problem in our time series analysis.

Learn how to deal with it! ๐Ÿ‘‡

๐Ÿงต

#DataScience #MachineLearning #Python
How to check if we have any missing values?

First, we can do a quick visual inspection. We can see that the line is not continuous at some points, which indicates the presence of missing values! โ˜ ๏ธ
The best scenario is that we don't really have missing values, but we just have the wrong frequency.

For example for stock data, we may be missing values on weekends. This can just be fixed by setting the frequency to business days or "B".
Read 11 tweets

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